Waveforms for the next generation wireless technology is always a hot topic. NOMA or Non-Orthogonal Multiple Access was one of the favoured candidates for 5G before the standards decided to continue using OFDMA. That hasn't dampened the spirits of die hard NOMA fans.
In one of the episodes of Wireless Future Podcast last year, Prof. Emil Björnson and Prof. Erik G. Larsson discussed the different forms of NOMA, and what their benefits and weaknesses are. You can listen/watch that here. A detailed research paper is available here.
A tutorial from 2020 proposed NOMA as a candidate for 6G here. NTT Docomo is a big supporter of NOMA and you can read more about NOMA in their article, announcement and research paper from 2013 proposing NOMA for 5G.
Let's not forget OTFS (Orthogonal Time Frequency and Space), it's also being widely discussed. You can read this article on 6G World featuring Professor Ronny Hadani of the University of Texas, Austin and co-founder of Cohere Wireless.
6GSymposium Spring 2022 edition featured a discussion on "Waveform Candidates For 6G". 5G World has a good write-up here. The following is an extract from the article:
At the May 2022 6G Symposium, three engineers gave presentations on possible technologies for 6G radios. One thing seems certain: machine learning (ML) will play a more significant role in 6G than it does in 5G.
Ronny Hadani of Cohere Technologies proposed orthogonal time-frequency space (OTFS), which he described as a combination of Radar and communications signals. OTFS creates a delay-Doppler radar image of a transmitter’s surroundings, by which software can predict what’s coming next as a transmitter moves.
Prof. Pei Xiao of the University of Surrey, a leading wireless research university, proposed a completely different concept that he called Sparse Code Multiple Access (SCMA). Xiao’s concept is based on using multiple subcarriers per user, where a user’s data is interleaved on two or three subcarriers.
Dani Korpi of Nokia Bell Labs discussed neither a specific modulation nor coding. Instead, he suggested that machine learning could be used to “learn” a waveform based on the full air interface, fram transmitter to receiver. Korpi discussed the roadmap in Figure above where ML replaces not only processing blocks, but the transmitter physical layer and constellation.
You can read the complete article here. The video of the discussion on "Waveform Candidates For 6G" is embedded below:
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